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Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (ηEX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and ηEX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the ηEX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and ηEX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal ηEX,tot mainly varies in the range of 10.40–11.40%.

This study assesses the socio-economic impacts in terms of value added, imports and employment of sugarcane-derived bioethanol production in Northeast (NE) Brazil. An extended inter-regional Input–Output (IO) model has been developed and is used to analyse three scenarios, all projected for 2020: a business-as-usual scenario (BaU) which projects current practices, and two scenarios that consider more efficient agricultural practices and processing efficiency (scenario A) and in addition an expansion of the sector into new areas (scenario B). By 2020 in all scenarios, value added and imports increase compared to the current situation. The value added by the sugarcane–ethanol sector in the NE region is 2.8 billion US$ in the BaU scenario, almost 4 billion US$ in scenario A, and 9.4 billion US$ in scenario B. The imports in the region will grow with 4% (BaU scenario), 38% (scenario A) and 262% (scenario B). This study shows that the large reduction of employment (114,000 jobs) due to the replacement of manual harvesting by mechanical harvesting can be offset by additional production and indirect effects. The total employment in the region by 2020 grows with 10% in scenario A (around 12,500 jobs) and 126% in scenario B (around 160,000 jobs). The indirect effects of sugarcane production in the NE are large in the rest of Brazil due to the import of inputs from these regions. The use of an extended inter-regional IO model can quantify direct and indirect socio-economic effects at regional level and can provide insight in the linkages between regions. The application of the model to NE Brazil has demonstrated significant positive socio-economic impacts that can be achieved when developing and expanding the sugarcane–ethanol sector in the region under the conditions studied here, not only for the NE region itself but also for the economy of the rest of Brazil.

Abstract With drastic fossil fuel depletion and environmental deterioration concerns, a move towards a more sustainable bioenergy-based economy is essential. Lately, the application of microwave (MW) irradiation for waste processing has been attracting interest globally. MW-assisted heating possesses several advantages such as the provision of high microwave energy into dielectric materials with deeper penetration for internal heat generation, showing beneficial features in improving the heating rate and reducing the reaction time. Consequently, the most recent literature regarding the applications of MW-assisted heating for biomass pretreatment as well as biofuel and bioenergy production was reviewed and consolidated in this study. An impressive increase in the product yield and improvement of the product properties are reported, with the use of MW-assisted heating in several conversion routes to produce biofuels. Despite being a promising technology for biofuel production, some major fundamental data of MW-assisted heating have not been comprehensively identified. Therefore, the feasibility of this technology for large-scale implementation is still subpar. Understanding the interaction between the feedstock and the microwave electromagnetic field, and the optimization of several operational and mechanical parameters are the two main keystones that would propel the industrialization of MW heating in the near future. This provides key insights leading to increased feasibility and more advanced application of MW heating.

Consider a situation where spatial heterogeneity leads to a cline, a gradual transition in dominance of two competing species. We first prove, in the context of a simplified competition–diffusion model, that there exists a stationary solution showing that the two species coexist in a transition zone. What happens then if, owing to climate change, the environmental profile moves with constant speed in space? We show here that, when the speed with which the environmental condition shifts exceeds the Fisher invasion speed of the advancing species, an expanding gap will form. We raise the question of whether such a phenomenon has been or can be observed.

This study evaluates quality function deployment-based innovation pathways for renewable energy projects. For this purpose, a 2-stage novel model is suggested. Firstly, the weights of the factors are calculated by considering Pythagorean fuzzy decision-making trial and evaluation laboratory methodology based on 2-tuple linguistic values. With the help of the impact-relation map, the activities, and immediate predecessors are identified. Within this context, five different paths are determined for this situation. Secondly, the duration pathways and innovation costs are calculated for renewable energy projects. The findings indicate that the activities A (customer expectations) and E (production requirement) play the most critical role because they take place in all different paths. Therefore, it is strongly recommended that renewable energy investment companies should take necessary actions to satisfy customer expectations, such as offering affordable products and providing necessary information for the use of these projects.

Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.